Microbial marker and application thereof in thyroid cancer diagnosis

Abstract

The invention discloses a microbial marker and an application of the microbial marker in diagnosis of thyroid cancer. The microbial marker comprises Catenibacterium. The microbial marker provided by the invention can effectively distinguish thyroid cancer patients from healthy people, and has high accuracy and stability.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedicine and molecular biology, and in particular to a microbial marker and its application in the diagnosis of thyroid cancer. Background Art

[0002] Thyroid cancer is a malignant tumor originating from the thyroid follicular epithelium or parafollicular epithelial cells. It is the most common malignant tumor of the head and neck. Types of thyroid cancer include papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC) and anaplastic thyroid carcinoma (ATC). The most common of these is PTC, which accounts for 85%-90% of all thyroid cancers. The incidence of thyroid cancer (TC) continues to rise worldwide and is the seventh most common cancer in China. High-risk groups include women and those with thyroid cancer gene mutations or family histories. According to statistics, TC is the second most common cancer in people aged 15-44 years. It is the fifth most common solid tumor in women and the sixteenth most common solid tumor in men.

[0003] The gut microbiota plays a key role in the immune response to cancer; the role of the microbiome in regulating the immune system and cancer progression has been extensively studied, but its association with thyroid cancer remains limited.

[0004] Existing studies have mainly focused on the differences in the microbiome between cancer patients and healthy people, and have not fully explored microbial markers in thyroid cancer patients. Current technology has not revealed significant microbial markers that can effectively distinguish thyroid cancer patients, resulting in limited application in cancer prediction and monitoring. Therefore, there is an urgent need for a technical means to effectively distinguish the microbiome characteristics of thyroid cancer patients in order to better reveal the pathogenesis of thyroid cancer and its biomarkers.

[0005] In view of this, the present invention is proposed. Summary of the invention

[0006] The purpose of the present invention is to provide a microbial marker and its application in the diagnosis of thyroid cancer. The microbial marker of the present invention can effectively distinguish thyroid cancer patients from healthy people, and has high accuracy and stability.

[0007] The present invention is achieved in that:

[0008] In a first aspect, the present invention provides the use of a reagent for detecting the abundance of a microbial marker in the preparation of a thyroid cancer prediction and / or diagnosis product, wherein the microbial marker includes Catenibacterium.

[0009] The present invention differentially analyzes the abundance of microorganisms in fecal samples of thyroid cancer patients and determines that Catenibacterium has the potential to be a thyroid cancer marker. It also verifies that it has good discrimination ability in both male and female patients. Therefore, Catenibacterium has high accuracy and stability as a thyroid cancer marker.

[0010] In some embodiments, the microbial marker is Catenibacterium. When the content of the microbial marker in the intestinal flora decreases, the person can be determined to be at high risk of thyroid cancer, and clinical diagnosis is recommended.

[0011] In some embodiments, the microorganism is derived from a stool sample of a thyroid cancer patient.

[0012] In some embodiments, the above reagents include reagents for one or more detection methods selected from the following: 16S sequencing, whole genome sequencing, quantitative polymerase chain reaction, PCR-pyrosequencing, fluorescence in situ hybridization, microarray, PCR-ELISA.

[0013] The present invention uses the abundance of a certain type of bacteria as a predictive and / or diagnostic indicator. Therefore, whether it is 16S sequencing quantification, whole genome sequencing, or other conventional means, they can all be used as measurement methods as long as the results of the abundance of intestinal flora can be obtained.

[0014] In some embodiments, the reagents include at least one of primers, probes, antisense oligonucleotides, aptamers, or antibodies specific to microbial markers.

[0015] In some embodiments, the above-mentioned product includes at least one of a reagent, a kit, a test paper, and a chip.

[0016] In a second aspect, the present invention provides a kit for the prevention and / or diagnosis of thyroid cancer, which comprises reagents for detecting the above-mentioned thyroid cancer microbial markers.

[0017] In some embodiments, the above reagents include reagents for detecting 16S rRNA of human intestinal microorganisms.

[0018] In a third aspect, the present invention provides the use of reagents for detecting the abundance of the above-mentioned microbial markers in constructing a computational model for diagnosing thyroid cancer.

[0019] In some embodiments, the input variable of the above-mentioned computational model is the abundance of the microbial marker.

[0020] The present invention has the following beneficial effects:

[0021] (1) The present invention achieves effective differentiation of thyroid cancer patients from healthy people through a variety of bioinformatics analysis methods (such as LEfSe, principal coordinate analysis, etc.), and Catenibacterium shows excellent diagnostic performance (AUC=0.943). In addition, in both male and female subsets, the ability of Catenibacterium to distinguish between the patient group and the healthy control group is within an acceptable range (TC female: AUC=0.803; TC male: AUC=0.935), indicating that the model has high stability.

[0022] (2) The intestinal thyroid cancer microbial molecular markers provided by the present invention provide new ideas and methods for the early diagnosis and detection of thyroid cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 The abundance of bacterial flora in fecal samples of healthy people and thyroid cancer patients sequenced by 16S rRNA in Example 1;

[0025] Figure 2 Schematic diagram of ROC curves for thyroid cancer patients, female patients and male patients in Example 2. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical scheme and advantages of the embodiments of the present invention clearer, the technical scheme in the embodiments of the present invention will be described clearly and completely below. If the specific conditions are not specified in the embodiments, they are carried out according to conventional conditions or conditions recommended by the manufacturer. If the manufacturer of the reagents or instruments used is not specified, they are all conventional products that can be purchased commercially.

[0027] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0028] Example 1

[0029] This example is the screening of intestinal thyroid cancer microbial molecular markers

[0030] 1. Research subjects

[0031] This cross-sectional study was conducted at West China Hospital, a tertiary general hospital in southwestern China, from March 2020 to March 2021. We consecutively recruited adult patients with suspected thyroid cancer before biopsy or surgery.

[0032] The inclusion criteria for TC patients were as follows:

[0033] (1) Patients who have been diagnosed with TC, whose primary tumor is located in the thyroid gland, and who are scheduled to undergo surgical treatment;

[0034] (2) patients who can understand the informed consent process;

[0035] (3) Patients who are willing to voluntarily participate in the study.

[0036] The exclusion criteria for TC patients were:

[0037] (1) Patients with severe mental illness or cognitive impairment who are unable to cooperate with the study;

[0038] (2) Patients with oral diseases (such as gingival inflammation or ulcers);

[0039] (3) patients with a history of dental restoration within the past six months;

[0040] (4) Taking antibiotics or drugs that regulate intestinal flora in the past 2 weeks;

[0041] (5) Gastrointestinal infection occurred in the past 2 weeks;

[0042] (6) history of digestive tract surgery, such as esophageal, gastrointestinal, or gallbladder surgery;

[0043] (7) Suffering from other serious systemic diseases, such as malignant tumors or uremia.

[0044] The elimination criteria for TC are:

[0045] (1) Patients who did not undergo surgery;

[0046] (2) patients who withdrew from the study during the study and whose TC diagnosis could not be confirmed by postoperative thyroid-related pathological diagnosis;

[0047] (3) Patients whose TC diagnosis could not be confirmed by postoperative thyroid-related pathological diagnosis.

[0048] The inclusion criteria for healthy controls (HC) were:

[0049] (1) No systemic diseases (such as hypertension, diabetes, systemic lupus erythematosus and other similar chronic diseases);

[0050] (2) no history of thyroid disease or TC;

[0051] (3) no history of previous oral surgery or oral diseases (such as periodontal disease and dental caries);

[0052] (4) No other concurrent tumor diseases.

[0053] The exclusion criteria for HC were as follows:

[0054] (1) Pregnancy or lactation;

[0055] (2) history of taking antibiotics, prebiotics, or probiotics within the previous month;

[0056] (3) history of gastrointestinal infection within the previous month;

[0057] (4) had undergone esophageal, gastrointestinal, gallbladder or other gastrointestinal surgery;

[0058] (5) Have severe mental illness or cognitive impairment.

[0059] Initially, a total of 102 patients with suspected TC were recruited. Based on histopathological results, 91 patients were confirmed to have primary TC after biopsy and surgery, and 11 patients were excluded from the study based on the exclusion criteria. We recruited 182 HCs from the wider community as comparison / control groups (Table 1). HCs were recruited after the TC group was determined to ensure post hoc selection. This approach ensured that HCs were matched to TC patients in terms of age and sex ratio, allowing appropriate comparisons between the groups.

[0060] Table 1 Age and gender characteristics of 182 subjects

[0061]

[0062] Summary: This study was based on cross-sectional data from a tertiary hospital in Southwest China. A total of 91 patients with confirmed thyroid cancer (64 females and 27 males) and 182 healthy controls (the gender and age ratio of healthy controls matched the patient group) were included to ensure the accuracy of comparative analysis. All subjects signed informed consent. This study has been approved by the Institutional Review Board of West China Hospital, Sichuan University (IRB number: 2021-0362). Informed consent was obtained from all participants in this study.

[0063] 2. Sample Collection

[0064] Fecal samples were collected from patients before surgery. The samples were immediately frozen at -80°C and DNA was extracted in the laboratory. High-throughput 16S rRNA sequencing technology was used to perform deep sequencing of the intestinal microbiome of the fecal samples.

[0065] 3. DNA Extraction

[0066] Follow the manufacturer's instructions for use Total genomic DNA samples were extracted using Soil DNA Kit (M5635; Omega Bio-tek, GA, USA) and stored at -80°C until further analysis. The extracted DNA was quantified using a fluorescence spectrophotometer (E6090 QuantiFluor; Promega, Madison, WI, USA). 1% agarose gel electrophoresis was used to determine the integrity and purity of the DNA.

[0067] 4. Library construction, sequencing and analysis of bacterial and fungal flora

[0068] The bacterial 16S rRNA gene fragment (V3-V4) was amplified using universal primers 338F (ACTCCTACGGGAGGCAGCA - SEQ ID NO. 1) and 806R (GGACTACHVGGGTWTCTAAT - SEQ ID NO. 2).

[0069] The amplicons were sequenced on an Illumina Novaseq 6000PE250 (Illumina, Inc., San Diego, CA, USA) platform. The sequencing data were processed with an integrated pipeline for bioinformatics analysis.

[0070] 5. Bioinformatics Analysis

[0071] After deduplication, the resulting sequences were quality filtered using fastp (version 0.19.6), and the sequences were merged using FLASH (version 1.2.11). Denoising was then performed using the DADA2 plugin in the Qiime2 (version 2020.2) pipeline with recommended parameters, which achieves single nucleotide resolution based on the error profile within the sample. The sequences denoised by DADA2 are often referred to as amplicon sequence variants (ASVs). To minimize the impact of sequencing depth on alpha and beta diversity metrics, the number of sequences per sample was rarefied to 31,770. Taxonomic assignment of ASVs was performed using the naive Bayes consensus classifier implemented in QIIME2 and the SILVA16S rRNA database (v138).

[0072] Species richness and evenness were assessed and compared using alpha diversity indices such as Chao1, ACE, Shannon, and Simpson. The sample size of each group was determined by cumulative curve analysis. Microbial composition analysis was presented by plotting Venn diagrams and structure diagrams at the genus level. To estimate the similarity between groups, beta diversity was measured by principal coordinate analysis (PCoA). Linear discriminant analysis (LDA) and effect size measurement (LEfSe) were used to investigate the differences in taxonomic characteristics between thyroid cancer (TC) patients and healthy controls (HC). Microbial taxa with LDA scores greater than 2 and P values ​​less than 0.05 were considered to be significantly different.

[0073] A random forest model was constructed to explore the potential discriminative ability of the intestinal microbiome in distinguishing TC patients from HC. The model performance was evaluated by receiver operating characteristic (ROC) curve analysis. The amplicon sequence variant (ASV) database was randomly split into two parts for secondary cross-validation, one part was used to build the random forest model (training set), and the other part was used for validation (test set), with a training set and test set ratio of 1:1. (It should be noted here that during the analysis, the software will default to dividing the samples into two groups as equally as possible. In the case of an odd number and without special requirements, the software will automatically handle this problem, which will make the number of samples in the training set and the test set differ by 1. For example, when the total number of samples is 91, the software automatically divides them into: 46 training sets and 45 test sets)

[0074] Correlations between gut microbiota and patient characteristics in the context of TC were analyzed by Spearman correlation heatmaps. All statistical analyses were performed using SPSS software v23.0 (IBM Corp., Armonk, NY) and R software version (4.0.3).

[0075] 6. Results

[0076] like Figure 1 As shown in the figure, in thyroid cancer patients, Blautia and Alistipes are the dominant bacteria in both male and female patients. The dominant bacteria unique to female patients are mainly Escherichia_Shigella and Schaalia, while the dominant bacteria unique to male patients include Holdemanella, Clostridium_sensu_stricto and Senegalimassilia. The dominant bacteria in normal people all contain Catenibacterium.

[0077] like Figure 2 As shown, Catenibacterium, as a differential bacterium, had a discrimination ability of AUC = 0.943 between the patient group and the healthy control group.

[0078] According to the same analysis method, this experiment also divided the samples into male and female subsets. In this scheme, the ratio of training set to test set was also 1:1. The analysis results showed that in both male and female subsets, the ability of Catenibacterium to distinguish between the patient group and the healthy control group was within an acceptable range (TC female: AUC = 0.803; TC male: AUC = 0.935), indicating that the model has high stability.

[0079] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Use of a reagent for detecting the abundance of microbial markers in the preparation of a product for predicting and / or diagnosing thyroid cancer, characterized in that: The microbial markers include Catenibacterium.

2. The use according to claim 1, characterized in that: The microorganisms were derived from stool samples of patients with thyroid cancer.

3. The use according to claim 1, characterized in that: The reagents include reagents for one or more detection methods selected from the group consisting of: 16S sequencing, whole genome sequencing, quantitative polymerase chain reaction, PCR-pyrophosphate sequencing, fluorescence in situ hybridization, microarray, and PCR-ELISA.

4. The use according to claim 3, characterized in that: The reagent includes at least one of a primer, a probe, an antisense oligonucleotide, an aptamer or an antibody specific to the microbial marker.

5. The use according to claim 4, characterized in that: The product includes at least one of a reagent, a test kit, a test paper, and a chip.

6. A kit for the prevention and / or diagnosis of thyroid cancer, characterized in that: The kit comprises a reagent for detecting the thyroid cancer microbial marker according to claim 1.

7. The kit according to claim 6, characterized in that The reagents include reagents for detecting 16SrRNA of human intestinal microorganisms.

8. Use of a reagent for detecting the abundance of the microbial marker described in claim 1 in constructing a computational model for diagnosing thyroid cancer.

9. The use according to claim 8, characterized in that: The input variable of the computational model is the abundance of the microbial marker.

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